First Machine-Learning Powered Workflow to Get Radiologists’ “Eyes on
Images” on Thousands of Studies More Quickly for Better Patient Care

December 08, 2015 06:50 AM Eastern Standard Time

MINNEAPOLIS--(BUSINESS WIRE)--vRad (Virtual Radiologic), an affiliate of MEDNAX,
Inc. (NYSE: MD) and the leading national teleradiology services and
telemedicine company, has successfully reached a critical milestone
outlined as part of its commitment to the growing branch of artificial
intelligence known as “Deep Learning” announced
in June 2015. vRad’s anonymized and
extensive clinical data sets and clinical expertise provided by their
medical leadership team drove the successful development of an algorithm
that reviews CT images in real-time and identifies potential
intracranial hemorrhaging (IH). After securing necessary regulatory
approvals, the next step will be to implement this process into vRad’s
patented telemedicine workflow immediately so radiologists will have the
ability to optimize diagnostic review time for these life-threatening
abnormalities. IH requires immediate medical treatment, otherwise it
quickly leads to increased pressure in the brain, and potentially
damaged brain tissue or death. vRad will commence the process to obtain
regulatory approval before the end of 2015.

“Once approved and implemented into vRad’s telemedicine platform, the
deep learning algorithm can immediately identify—or ‘recall’—a potential
IH and automatically prioritize that patient’s study so that it is
reviewed by the most appropriate radiologist more quickly,” said Shannon
Werb, Chief Information Officer of vRad. “While many are talking about
machine-assisted diagnostics, like IBM’s
Watson and Google,
vRad will be the first to leverage deep learning in a real-time practice
environment. Our radiologists will have an additional tool to help
maximize the speed at which they can get ‘eyes on images’ to determine
if there is a diagnosis of IH. This milestone shows how the right
clinical and technical collaboration can empower radiologists, increase
their time being doctors and diagnosticians—and ultimately improve
patient outcomes.”

vRad’s physicians used anonymized data from vRad’s extensive clinical
database to train AI software to search, analyze and correctly recall
positive cases of IH. vRad interprets approximately ninety-thousand head
CTs monthly, thousands of which are diagnosed with IH. With the
deep-learning-powered workflow, all potential IH cases recalled by the
algorithm will be “flagged” so that the patient’s study can be
automatically prioritized within the radiologist’s reading queue. vRad
can then assign cases with potential IH to the most appropriately
trained/experienced radiologist (e.g., a neuroradiologist), so they can
direct their attention to the image, diagnose the condition, and relay
critical findings to the attending physician as quickly as possible.
Adding this capability to other priority-based workflows, including the
practice’s Trauma Protocol, will allow vRad to target radiologists’
“eyes on the images” in less than the current average of four minutes
after receipt from a client’s referring facility. Based on most recent
test results for recall and precision, vRad expects that over five
thousand studies could be identified for potential IH from the
patent-pending IH workflow in 2016, creating faster delivery of care to
those patients.

“The combination of deep learning technology with our large clinical
datasets and expertise serves as a model of how cutting-edge technology
can be used to complement—not supplant—clinicians and improve care,”
said Dr. Benjamin Strong, vRad’s Chief Medical Officer. “We are
encouraged by the algorithm’s precision performance to date in the test
environment and will continue to focus on continuous improvement of the
algorithm’s recall levels of IH so that once it is implemented, we can
optimize the study distribution workflow. We look forward to extending
deep learning to additional life-threatening abnormalities so vRad’s
clinicians can deliver high-quality, accurate diagnoses to referring
physicians as quickly as possible.”

About vRad

vRad (Virtual Radiologic) is the leading national teleradiology services
and telemedicine company with over 350 U.S. board-certified and eligible
physicians, 75% of whom are subspecialty trained. Its clinical expertise
and evidence-based insight help clients make better decisions about the
health of their patients and their imaging services. vRad is an
affiliate of MEDNAX,
Inc. (NYSE: MD), a national medical group specializing in neonatal,
anesthesia, maternal-fetal, pediatric cardiology and other pediatric
physicians services.

vRad interprets and processes patient imaging studies on the world’s
largest and most advanced teleradiology PACS for 2,100+ client hospital,
health system and radiology group facilities in all 50 states. The
practice has 15 issued patents for innovation in telemedicine workflow,
and is a recognized leader in imaging analytics and deep
learning-assisted diagnostics. It is also a past winner of Frost
& Sullivan’s Visionary Innovation Award for Medical Imaging
Analytics (North America). For more information, please visit www.vrad.com.
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